code: def setUp(self) -> None:
      self.random_state = 0
      d: dict = load_breast_cancer()
      X: DataFrame = DataFrame(d["data"], columns=d["feature_names"])
      self.col_ordinal = X.columns.to_list()
      np.random.seed(self.random_state)
      s = np.array(["a", "b", "c"])
      X["cat alpha"] = s[np.random.randint(0, 3, len(X))]
      X["cat num"] = np.random.randint(0, 3, len(X))
      self.col_categorical = ["cat alpha", "cat num"]
      s = np.array(["a", "b"])
      X["bin alpha"] = s[np.random.randint(0, 2, len(X))]
      X["bin num"] = np.random.randint(0, 2, len(X))
      self.col_binary = ["bin alpha", "bin num"]
      self.X = X
      self.y: ndarray = d["target"]
      (
          self.X_train,
          self.X_test,
          self.y_train,
          self.y_test,
      ) = train_test_split(
          self.X, self.y, test_size=0.4, random_state=self.random_state
      )
  
file_path: AIX360/tests/rbm/test_Feature_Binarizer_From_Trees.py
imports: from unittest import TestCase
  import numpy
  from numpy import ndarray
  from pandas import DataFrame
  from sklearn.datasets import load_breast_cancer
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import OneHotEncoder, StandardScaler
  from aix360.algorithms.rbm import FeatureBinarizerFromTrees
label_desc: Use image node_id 1 for calling the TestFeatureBinarizerFromTrees obj's underlying member method code with example usage: obj.setUp() without return types